With the need for huge amounts of labeled data, synthetic datasets have recently gained considerable interest. Some notable examples are GTA5 [1]} and SYNTHIA [2]} for semantic segmentation. However, there is a non-negligible domain gap between the synthetic and the real worlds, as mentioned above. Many techniques try to fill this gap, using both supervised and unsupervised approaches. A more exhaustive survey about deep learning da techniques is provided in sec:back:domainadaptation and in [3]}. For example, authors in [4]} proposed an unsupervised domain adaptation solution for the segmentation task. Authors in [5]} created jta, a synthetic dataset gathered from the highly photo-realistic video game GTA V. They demonstrated that it is possible to reach excellent results on tasks such as people tracking and pose estimation when validating on real data. In this work, we extend this dataset, making it suitable for the pedestrian detection task. Authors in [6]}, [7]} have also focused on learning features from synthetic data for the pedestrian detection task. Still, they did not consider deep learning approaches, exploring only traditional detection techniques. In [8]}, instead, the authors employed a synthetic dataset to train a CNN able to detect objects belonging to different classes in a video. This cnn is responsible only for the classification of the objects, while their detection relies on a background subtraction algorithm based on Gaussian Mixture Models (GMMs). The performance of this approach over real-world scenarios was evaluated employing two pedestrian detection datasets, one of which, the 2D MOT 2015 [9]}, is an older version of the dataset we used to carry out our experiments. To the best of our knowledge, [10]} and [8]} are the closest works to ours. In particular, they also used GTA V as the source for the acquisition of the synthetic data, but they focused their efforts on the vehicle detection task.
